Description
Know how to use quantum computing solutions involving artificial intelligence (AI) algorithms and applications across different disciplines. Quantum solutions involve building quantum algorithms that improve computational tasks within quantum computing, AI, data science, and machine learning. As opposed to quantum computer innovation, quantum solutions offer automation, cost reduction, and other efficiencies to the problems they tackle. Starting with the basics, this book covers subsystems and properties as well as the information processing network before covering quantum simulators. Solutions such as the Traveling Salesman Problem, quantum cryptography, scheduling, and cybersecurity are discussed in step-by-step detail. The book presents code samples based on real-life problems in a variety of industries, such as risk assessment and fraud detection in banking. In pharma, you will look at drug discovery and protein-folding solutions. Supply chain optimization and purchasing solutions are presented in the manufacturing domain. In the area of utilities, energy distribution and optimization problems and solutions are explained. Advertising scheduling and revenue optimization solutions are included from media and technology verticals. What You Will Learn Understand the mathematics behind quantum computing Know the solution benefits, such as automation, cost reduction, and efficiencies Be familiar with the quantum subsystems and properties, including states, protocols, operations, and transformations Be aware of the quantum classification algorithms: classifiers, and support and sparse support vector machines Use AI algorithms, including probability, walks, search, deep learning, and parallelism Who This Book Is For Developers in Python and other languages interested in quantum solutions. The secondary audience includes IT professionals and academia in mathematics and physics. A tertiary audience is those in industry verticals such as manufacturing, banking, and pharma. Part 1: Introduction Chapter 1: Quantum Computing Solutions Overview 1.1 Real-Life Problems and Solutions 1.2 Solution Benefits 1.2.1 Automation of manual, semi manual processes 1.2.3 Cost Reduction and Improving Profit 1.2.3 Improving Efficiencies and reducing the defects 1.3 Solutions 1.3.1 Cryptography 1.3.2 Optimization 1.3.3 Cyber Security Chapter 2: Mathematics behind Quantum Computing 2.1 Quantum Operators 2.2 Sets 2.3 Vectors 2.4 Matrices 2.5 Tensors Part 2: Quantum Computing Basics Chapter 3: Quantum SubSystems and Properties 3.1 Single Qubit System 3.2 Multiple Qubit System 3.3 Quantum States 3.4 Quantum Protocols 3.5 Quantum Operations 3.6 Quantum Transformations Chapter 4: Quantum Information Processing Framework 4.1 Quantum Circuits 4.2 Quantum Communication 4.3 Quantum Noise 4.4 Quantum Error Correction 4.5 Limitations of Quantum Computing 4.6 Quantum Algorithms 4.6.1 Duetsch-Jozsa Algorithm 4.6.2 Simon’s Algorithm 4.6.3 Shor’s Algorithm 4.6.4 Grover’s Algorithm 4.7 Quantum Subroutines Chapter 5: Quantum Simulators 5.1 Quantum Languages 5.2 Qubit Measurement 5.3 Quantum Instruction Sets 5.4 Full stack Universal Quantum Simulator 5.5 Quantum Assembly Programming 5.6 Quantum Hardware Platforms Part 3: Quantum Solutions Chapter 6: Quantum Optimization Algorithms 6.1 Approximate optimization algorithms 6.2 Combinatorial Optimization 6.3 SemiDefinite Programming 6.4 Quantum NP (BQNP) Chapter 7: Quantum Algorithms 7.1 Quantum Least Squares fitting 7.2 Quantum semidefinite programming 7.3 Quantum sort 7.4 Quantum Eigen Solvers Chapter 8: Quantum Neural Network Algorithms 8.1 Quantum ANN 8.2 Quantum Associative Memory 8.3 Quantum Dots 8.4 Quantum Random Access Memory 8.5 Quantum GAN Chapter 9: Quantum Classification Algorithms 9.1 Classifiers 9.2 Support Vector Machines 9.3 Sparse Support Vector machines Chapter 10: Quantum Data Processing 10.1 Quantum K-Means 10.2 Quantum K-Medians 10.3 Quantum Clustering 10.4 Quantum Manifold Embedding Chapter 11: Quantum AI Algorithms 11.1 Quantum Probability 11.2 Quantum Walks 11.3 Quantum Search 11.4 Quantum Deep Learning 11.5 Quantum Parallelism Chapter 12: Quantum Solutions 12.1 Traveling Salesman Problem 12.2 Scheduling 12.3 Fraud Detection 12.4 Distribution solutions Chapter 13: Evolving Quantum Solutions 13.1 Quantum Annealing 13.2 Quantum Key Distribution 13.3 Quantum Teleportation Chapter 14: Next Steps 14.1 Best Practices 14.2 Case Studies 14.3 Quantum Computing News Sources 14.3 Quantum Ware – Future
